Improvement of Korean Sign Language Recognition System by User Adaptation

사용자 적응을 통한 한국 수화 인식 시스템의 개선

This paper presents user adaptation methods to overcome limitations of a user-independent model and a user-dependent model in a Korean sign language recognition system. To adapt model parameters for unobserved states in hidden Markov models, we introduce new methods based on motion similarity and prediction from adaptation history so that we can achieve faster adaption and higher recognition rates comparing with previous methods.